Back to Search
Start Over
A pipeline to analyse time-course gene expression data
- Source :
- F1000Research, F1000Research, Faculty of 1000, 2020, 9, pp.1447. ⟨10.12688/f1000research.27262.1⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; The phenotypic diversity of cells is governed by a complex equilibrium between their genetic identity and their environmental interactions: Understanding the dynamics of gene expression is a fundamental question of biology. However, analysing time-course transcriptomic data raises unique challenging statistical and computational questions, requiring the development of novel methods and software. This workflow provides a step-by-step tutorial of the methodology used to analyse time-course data: (1) quality control and normalization of the dataset; (2) differential expression analysis using functional data analysis; (3) clustering of time-course data; (4) interpreting clusters with GO term and KEGG pathway enrichment analysis. As a case study, we apply this workflow to time-course transcriptomic data from mice exposed to four strains of influenza to showcase every step of the pipeline.
- Subjects :
- 0301 basic medicine
Normalization (statistics)
Differential expression analysis
General Immunology and Microbiology
business.industry
Computer science
[SDV]Life Sciences [q-bio]
Functional data analysis
General Medicine
Computational biology
General Biochemistry, Genetics and Molecular Biology
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Workflow
Software
Time course
Gene expression
General Pharmacology, Toxicology and Pharmaceutics
business
Cluster analysis
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 20461402
- Database :
- OpenAIRE
- Journal :
- F1000Research, F1000Research, Faculty of 1000, 2020, 9, pp.1447. ⟨10.12688/f1000research.27262.1⟩
- Accession number :
- edsair.doi.dedup.....9b7bd6fd832d267368a1f05a268d3b9b